abstract = "This paper presents grammatical evolution (GE) as an
approach to select and combine features for detecting
epileptic oscillations within clinical intracranial
electroencephalogram (iEEG) recordings of patients with
epilepsy. Clinical iEEG is used in preoperative
evaluations of a patient who may have surgery to treat
epileptic seizures. Literature suggests that
pathological oscillations may indicate the region(s) of
brain that cause epileptic seizures, which could be
surgically removed for therapy. If this presumption is
true, then the effectiveness of surgical treatment
could depend on the effectiveness in pinpointing
critically diseased brain, which in turn depends on the
most accurate detection of pathological oscillations.
Moreover, the accuracy of detecting pathological
oscillations depends greatly on the selected feature(s)
that must objectively distinguish epileptic events from
average activity, a task that visual review is
inevitably too subjective and insufficient to resolve.
Consequently, this work suggests an automated algorithm
that incorporates grammatical evolution (GE) to
construct the most sufficient feature(s) to detect
epileptic oscillations within the iEEG of a patient. We
estimate the performance of GE relative to three
alternative methods of selecting or combining features
that distinguish an epileptic gamma (~65-95 Hz)
oscillation from normal activity: forward sequential
feature-selection, backward sequential
feature-selection, and genetic programming. We
demonstrate that a detector with a grammatically
evolved feature exhibits a sensitivity and selectivity
that is comparable to a previous detector with a
genetically programmed feature, making GE a useful
alternative to designing detectors.",